We propose a content-aware negative sampling strategy that favors negatives sufficiently similar to a positive instance in the CNN embedding space; we call our approach CANS-Similar. Our approach is inspired by [1]} who showed that randomly initialized CNNs provide a strong prior over natural images due to the inductive bias already built into the design of the CNNs. This suggests that our local feature embeddings \(\mathbf {x}^v\)  (and \(\mathbf {x}^a\) ) can capture the underlying statistical regularities in video clips (and audio streams) right from the beginning, which can be sufficient to assess the similarity/dissimilarity between clips. Therefore, the distance measured on them can approximate content dissimilarity well (and this will improve as the training progresses).
